Objective To compare the efficacy of the single tube (ST) and double tube (DT) for closed thoracic drainage after lobectomy. Methods The PubMed, Medline, EMbase, Web of Science, CNKI, Wanfang Database, VIP database and CBMdisc from inception to March 30, 2018 were searched by computer to identify randomized controlled trial (RCT) about ST and DT drainage after lobectomy. Based on inclusion and exclusion criteria the literature was screened. Meta-analysis was performed using RevMan 5.3 software. Results Twelve RCTs were enrolled in this meta-analysis, including 1 442 patients. Compared with the patients using DT after lobectomy, the patients using ST had significantly less postoperative pain (MD=–0.64, 95%CI –0.71 to –0.56, P<0.000 01) and shorter duration of drainage (MD=–0.62, 95%CI –0.78 to –0.46, P<0.000 01) and hospital stay (MD=–0.55, 95%CI –0.80 to –0.29, P<0.000 1). Besides, there was no significant difference in postoperative complications (RR=1.11, 95%CI 0.83 to 1.49, P=0.49), air leaks (RD=0.03, 95%CI –0.02 to 0.08, P=0.19) and the redrainage rate (RR=0.89, 95%CI 0.51 to 1.54, P=0.67). ConclusionST drainage after lobectomy is effective, which reduces postoperative pain and duration of hospital stay and drainage, and moreover, does not increase the postoperative complications and redrainage rate.
With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.
